where \(\mathcal {L}_{other}\)  means other loss functions presented in these works. \(\lambda _{1}\)  and \(\lambda _{2}\)  denote the weight factor of these loss functions. These methods have achieved encouraging results in natural image segmentation[1]}, [2]} and salience object detection [3]}, [4]}, etc. But for abdominal multi-organ segmentation, learning from scribble is also a very challenging task. Different from the above, we propose a new regularization term to train deep networks for weakly supervised abdominal multi-organ segmentation.
